scholarly journals Volitive Clan PSO - An Approach for Dynamic Optimization Combining Particle Swarm Optimization and Fish School Search

10.5772/30362 ◽  
2012 ◽  
Author(s):  
George M. ◽  
Carmelo J. A. Bastos-Filho ◽  
Fernando B. de Lima-Neto
Keyword(s):  
Particle Swarm Optimization ◽  
Dynamic Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Fish School

scholarly journals REDUCTION OF ACTIVE POWER LOSS BY VOLITION PARTICLE SWARM OPTIMIZATION

2018 ◽  
Vol 6 (6) ◽  
pp. 346-356
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Reactive Power ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test System ◽  
Swarm Optimization ◽  
Fish School ◽  
Exploration Exploitation ◽  
Power Problem

This paper projects Volition Particle Swarm Optimization (VP) algorithm for solving optimal reactive power problem. Particle Swarm Optimization algorithm (PSO) has been hybridized with the Fish School Search (FSS) algorithm to improve the capability of the algorithm. FSS presents an operator, called as collective volition operator, which is capable to auto-regulate the exploration-exploitation trade-off during the algorithm execution. Since the PSO algorithm converges faster than FSS but cannot auto-adapt the granularity of the search, we believe the FSS volition operator can be applied to the PSO in order to mitigate this PSO weakness and improve the performance of the PSO for dynamic optimization problems. In order to evaluate the efficiency of the proposed Volition Particle Swarm Optimization (VP) algorithm, it has been tested in standard IEEE 30 bus test system and compared to other reported standard algorithms.  Simulation results show that Volition Particle Swarm Optimization (VP) algorithm is more efficient then other algorithms in reducing the real power losses with control variables are within the limits.

Dual-archive-based particle swarm optimization for dynamic optimization

2019 ◽  
Vol 85 ◽  
pp. 105876
Author(s):  
Xiao-Fang Liu ◽  
Yu-Ren Zhou ◽  
Xue Yu ◽  
Ying Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Dynamic Optimization ◽  
Particle Swarm ◽  
Swarm Optimization

Reverse Search Particle Swarm Optimization to Deal with Dynamic Optimization Problems

2014 ◽  
Vol 571-572 ◽  
pp. 245-251
Author(s):  
Li Chen ◽  
Wei Jiang Wang ◽  
Lei Yao
Keyword(s):  
Particle Swarm Optimization ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
Fitness Function ◽  
Dynamic Environment ◽  
Particle Swarm ◽  
Dynamic Optimization Problems ◽  
Swarm Optimization ◽  
Promising Area ◽  
Coarse Search

Multiswarm approaches are used in many literatures to deal with dynamic optimization problems (DOPs). Each swarm tries to find promising areas where usually peaks lie and many good results have been obtained. However, steep peaks are difficult to be found with multiswarm approaches , which hinders the performance of the algorithm to be improved furtherly. Aiming at the bottleneck, the paper introduces the idea of sequential niche technique to traditional multiswarm approach and thus proposes a novel algorithm called reverse space search multiswarm particle swarm optimization (RSPSO) for DOPs. RSPSO uses the information of the peaks found by coarse search of traditional multiswarm approach to modify the original fitness function. A newly generated subswarm - reverse search subswarm evolves with the modified fitness function, at the same time, other subswarms using traditional mltiswarm approach still evolve. Two kinds of subswarm evolve in cooperation. Reverse search subswarm tends to find much steeper peak and so more promising area where peaks lie is explored. Elaborated experiments on MPB show the introduction of reverse search enhances the ability of finding peaks , the performance of RSPSO significantly outperforms traditional multiswarm approaches and it has better robustness to adapt to dynamic environment with wider-range change severity.

scholarly journals Dynamic Optimization with Particle Swarms (DOPS): A meta-heuristic for parameter estimation in biochemical models

2017 ◽  
Author(s):  
Adithya Sagar ◽  
Rachel LeCover ◽  
Christine Shoemaker ◽  
Jeffrey Varner
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Dynamic Optimization ◽  
Real World ◽  
Particle Swarm ◽  
Coagulation Cascade ◽  
Benchmark Problems ◽  
Test Functions ◽  
Swarm Optimization ◽  
Particle Swarms

AbstractBackgroundMathematical modeling is a powerful tool to analyze, and ultimately design biochemical networks. However, the estimation of the parameters that appear in biochemical models is a significant challenge. Parameter estimation typically involves expensive function evaluations and noisy data, making it difficult to quickly obtain optimal solutions. Further, biochemical models often have many local extrema which further complicates parameter estimation. Toward these challenges, we developed Dynamic Optimization with Particle Swarms (DOPS), a novel hybrid meta-heuristic that combined multi-swarm particle swarm optimization with dynamically dimensioned search (DDS). DOPS uses a multi-swarm particle swarm optimization technique to generate candidate solution vectors, the best of which is then greedily updated using dynamically dimensioned search.ResultsWe tested DOPS using classic optimization test functions, biochemical benchmark problems and real-world biochemical models. We performed trials with function evaluations per trial, and compared the performance of DOPS with other commonly used meta-heuristics such as differential evolution (DE), simulated annealing (SA) and dynamically dimensioned search (DDS). On average, DOPS outperformed other common meta-heuristics on the optimization test functions, benchmark problems and a real-world model of the human coagulation cascade.ConclusionsDOPS is a promising meta-heuristic approach for the estimation of biochemical model parameters in relatively few function evaluations. DOPS source code is available for download under a MIT license at http://www.varnerlab.org.

A novel hybrid adaptive collaborative approach based on particle swarm optimization and local search for dynamic optimization problems

2015 ◽  
Vol 32 ◽  
pp. 432-448 ◽  
Author(s):  
Ali Sharifi ◽  
Javidan Kazemi Kordestani ◽  
Mahshid Mahdaviani ◽  
Mohammad Reza Meybodi
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Collaborative Approach ◽  
Dynamic Optimization Problems ◽  
Swarm Optimization

Finite element model updating using fish school search and volitive particle swarm optimization

2015 ◽  
Vol 22 (4) ◽  
pp. 361-376 ◽  
Author(s):  
I. Boulkaibet ◽  
L. Mthembu ◽  
F. De Lima Neto ◽  
T. Marwala
Keyword(s):  
Finite Element ◽  
Particle Swarm Optimization ◽  
Finite Element Model ◽  
Model Updating ◽  
Particle Swarm ◽  
Element Model ◽  
Finite Element Model Updating ◽  
Swarm Optimization ◽  
Fish School
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